Graph-based models could handle variant inputs and thus have attracted much attention in chemistry.
[1]} described a molecule by a set of n atoms with nuclear charges and atomic positions and proposed to use continuous filter convolutional layers to model local correlations.
The Schnet presented excellent performance on molecules property prediction.
[2]} described a crystal using an undirected graph, where the vertices and edges correspond to the atoms and bonds, respectively.
The atomic interactions were captured by a specific convolution layer, and a pooling layer embedded the graph into the feature space.
The CGCNN model presented excellent performance on a broad array of properties, including formation energy, bandgap, Fermi energy, and elastic properties.
[3]} adapted the original CGCNN using multitask learning [4]} , and their experiments showed that sharing parameters in the lower level improved the prediction performance.
Some researchers tried to integrate additional knowledge in the model for better performance.
[5]} developed an universal model to predict property for both molecules and crystals by incorporating state variables, such as temperature, pressure, and entropy.
[6]} added a relative position matrix into the model and provided a mechanism to handle the feature update process.
Their 3DGCN model showed good performance on predicting molecular properties and biochemical activities.
